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{ \Large \bf Abstract}
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Ill posed problems can be found in many knowledge branches, like physics, chemistry,
medicine and engineering. In general, these problems have complex and not unique solutions.
Many mathematics models have been proposed to solve this kind of problem. In special,
methods based on regularization theory are closely to this work due their proximity
with neural networks area.
Inverse problems can be understood as a special ill posed problems class. Usually they have 
several solutions, making their analysis very complex.
The Radial Basis Function Neural Networks employment to solve inverse problems, more
specifically to determine intermolecular potential of chemistry systems through 
virial coefficients, is the main aim of this work. Moreover, it is discussed how 
stochastic neural networks could contribute to virial inversion when it is described by
a energy function.
The intermolecular potentials determined using RBF nets are impressive and 
became themselves better when using only rare-gas systems. On the other hand, stochastics 
networks provided reasonable results but not so stables. Local regularization  
and different cooling schedules seems to be good alternatives to improve the results.